Fusion of Hyperspectral and LiDAR Data for Landscape Visual Quality Assessment

Landscape visual quality is an important factor associated with daily experiences and influences our quality of life. In this work, the authors present a method of fusing airborne hyperspectral and mapping light detection and ranging (LiDAR) data for landscape visual quality assessment. From the fused hyperspectral and LiDAR data, classification and depth images at any location can be obtained, enabling physical features such as land-cover properties and openness to be quantified. The relationship between physical features and human landscape preferences is learned using least absolute shrinkage and selection operator (LASSO) regression. The proposed method is applied to the hyperspectral and LiDAR datasets provided for the 2013 IEEE GRSS Data Fusion Contest. The results showed that the proposed method successfully learned a human perception model that enables the prediction of landscape visual quality at any viewpoint for a given demographic used for training. This work is expected to contribute to automatic landscape assessment and optimal spatial planning using remote sensing data.